Efficient search with posterior probability estimates in HMM-based speech recognition

In this paper we present the methods we developed to estimate posterior probabilities for HMM states in continuous and discrete HMM-based speech recognition systems and several ways to speed up decoding by using these posterior probability estimates. The proposed pruning techniques are state deactivation pruning (SDP), similar to an approach proposed for hybrid recognition systems, and a novel posteriori-based lookahead technique, posteriori lookahead pruning (PLP), that evaluates future posteriors in order to exclude unlikely HMM states as early as possible during search. By applying the proposed methods we managed to vastly reduce the decoding time consumed by our time-synchronous Viterbi-decoder for recognition systems based on the Verbmobil and the Wall Street Journal database with hardly any additional search error.